Greenspace and Land Cover Diversity During Pregnancy in a Rural Region, and Associations With Birth Outcomes

Abstract Beneficial effects on health outcomes have been observed from exposure to spaces with substantial green vegetation (“greenspace”). This includes studies of greenspace exposure on birth outcomes; however, these have been conducted largely in urban regions. We characterized residential exposure to greenspace and land cover diversity during pregnancy in rural northern New England, USA, investigating whether variation in greenspace or diversity related to newborn outcomes. Five landscape variables (greenspace land cover, land cover diversity, impervious surface area, tree canopy cover, and the Normalized Difference Vegetation Index) were aggregated within six circular zones of radii from 100 to 3,000 m around residential addresses, and distance to conservation land was measured, providing a total of 31 greenspace and diversity metrics. Four birth outcomes along with potentially confounding variables were obtained from 1,440 participants in the New Hampshire Birth Cohort Study. Higher greenspace land cover up to 3,000 m was associated with larger newborn head circumference, while impervious surface area (non‐greenspace) had the opposite association. Further, birth length was positively associated with land cover diversity. These findings support beneficial health impacts of greenspace exposure observed in urban regions for certain health outcomes, such as newborn head circumference and length but not others such as birthweight and gestational age. Further our results indicate that larger radius buffer zones may be needed to characterize the rural landscape. Vegetation indices may not be interchangeable with other greenspace metrics such as land cover and impervious surface area in rural landscapes.


Introduction
Figure S1 shows the correlation between each pair of greenspace metrics, including all combinations of metric and buffer radius.Table S1 provides more details about the eight models that are highlighted as significant (p < 0.05) in Table 3.

Sensitivity Analysis: Alternative Methods and Data
As described in Section 2 of the main text of this paper (Materials and Methods), the birth cohort dataset had missing values for some covariates in some data records (Table 1).These were imputed using the Multivariate Imputation by Chained Equations algorithm (MICE), which is based on predictive mean matching for continuous data, and logistic regression, polytomous logistic regression, and proportional odds for binary and categorical data (Van Buuren and Groothuis-Oudshoorn, 2011).In addition, there were substantial numbers of outliers in some variables, which suggested the use of a regression algorithm that is particularly robust to outliers: the Robust Fitting of Linear Models (rlm) algorithm (Hampel et al., 2011;Huber, 2004;Marazzi, 1993).
To ensure that the results reported in the main text of this paper are not an artifact of the particular methods used, we performed a sensitivity analysis using (a) only the 827 birth cohort data records with no missing values in any of the covariates, to eliminate the need for imputation; and (b) the more widely-used glm function in R for generalized linear models (Dobson, 1990).The results were broadly similar to those of the original analysis, although only two models achieved significance at α = 0.05, possibly due to the decreased sample size (n = 827 vs 1440 in the original study).The model for length zscore with land cover diversity in a 3000 m radius had a p-value of 0.0475; this model had the highest significance in the original analysis (p = 0.0030).The length z-score model with land cover diversity in a 500 m radius also was significant in this sensitivity analysis (p = 0.0388), despite not quite achieving significance at α = 0.05 in the original work (p = 0.0556).No other models were significant in this alternative analysis, confirming the original finding of a general scarcity of significant relationships outside of the land cover diversity & length z-score models.
We also conducted an assessment of two additional birth outcomes, to determine whether either preterm births or low birth weights were significantly associated with any of the greenspace metrics.Both of these were modeled using binomial versions of the generalized linear model algorithm in R, for the full set of 1440 birth cohort participants, and using each of the 31 greenspace metrics.As shown in Table S2, none of these 62 models were significant at α = 0.05.Note, for comparison, that two related birth outcomes discussed in the main text of this paper (gestational age and weight zscore) also were not significantly associated with any of the greenspace metrics (Table 3).

Table S1 .
Regression details for models with greenspace p-values < 0.05 (highlighted in Table3).SE = standard error of β^; RMSE=root mean squared error; MAE=mean absolute error.Slight differences in degrees of freedom for the t-statistic are due to the cross validation process, and to the presence of one additional variable (delivery type) in the models for head circumference.

Table S2 .
Model significance (p-value) for 31 greenspace metrics and two birth outcomes: preterm birth and low birth weight.